Patient-specific calibration of pain quantification

ABSTRACT

This document discusses, among other things, systems and methods for managing pain in a patient. A system may include sensors to sense physiological or functional signals, and a pain analyzer that generates a pain score using the sensed physiological or functional signals and a fusion model. The system includes a calibration module that calibrates the fusion model based on measurements from the sensed physiological or functional signals and a reference pain quantification corresponding to multiple pain intensities. A pain score may be generated using the calibrated fusion model. The system can additionally include a neurostimulator that controls the delivery of pain therapy by adjusting one or more stimulation parameters based on the pain score.

CLAIM OF PRIORITY

This application claims the benefit of priority under 35 U.S.C. § 119(e)of U.S. Provisional Patent Application Ser. No. 62/445,095, filed onJan. 11, 2017, which is herein incorporated by reference in itsentirety.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related to commonly assigned U.S. Provisional PatentApplication Ser. No. 62/445,053, entitled “PAIN MANAGEMENT USINGCARDIOVASCULAR PARAMETERS”, filed on Jan. 11, 2017, U.S. ProvisionalPatent Application Ser. No. 62/445,061, entitled “PAIN MANAGEMENT BASEDON BRAIN ACTIVITY MONITORING”, filed on Jan. 11, 2017, U.S. ProvisionalPatent Application Ser. No. 62/445,069, entitled “PAIN MANAGEMENT BASEDON RESPIRATION-MEDIATED HEART RATES”, filed on Jan. 11, 2017, U.S.Provisional Patent Application Ser. No. 62/445,075, entitled “PAINMANAGEMENT BASED ON FUNCTIONAL MEASUREMENTS”, filed on Jan. 11, 2017,U.S. Provisional Patent Application Ser. No. 62/445,082, entitled “PAINMANAGEMENT BASED ON EMOTIONAL EXPRESSION MEASUREMENTS”, filed on Jan.11, 2017, U.S. Provisional Patent Application Ser. No. 62/445,092,entitled “PAIN MANAGEMENT BASED ON MUSCLE TENSION MEASUREMENTS”, filedon Jan. 11, 2017, U.S. Provisional Patent Application Ser. No.62/395,641, entitled “METHOD AND APPARATUS FOR PAIN MANAGEMENT USINGHEART SOUNDS”, filed on Sep. 16, 2016, U.S. Provisional PatentApplication Ser. No. 62/400,313, entitled “SYSTEMS AND METHODS FORCLOSED-LOOP PAIN MANAGEMENT”, filed on Sep. 27, 2016, U.S. ProvisionalPatent Application Ser. No. 62/400,336, entitled “METHOD AND APPARATUSFOR PAIN MANAGEMENT USING OBJECTIVE PAIN MEASURE”, filed on Sep. 27,2016, U.S. Provisional Patent Application Ser. No. 62/412,587, entitled“METHOD AND APPARATUS FOR PAIN CONTROL USING BAROREFLEX SENSITIVITYDURING POSTURE CHANGE”, filed on Oct. 25, 2016, which are incorporatedby reference in their entirety.

TECHNICAL FIELD

This document relates generally to medical systems and more particularlyto systems, devices, and methods for pain management.

BACKGROUND

Pain is one of the most common and among the most personally compellingreasons for seeking medical attention, and consumes considerablehealthcare resources each year. The relation between etiology,underlying mechanisms and the specific symptoms and signs related topainful disorders is complex. Pain in an individual patient may beproduced by more than one mechanism.

Chronic pain, such as pain present most of the time for a period of sixmonths or longer during the prior year, is a highly pervasive complaintand consistently associated with psychological illness. Chronic pain mayoriginate with a trauma, injury or infection, or there may be an ongoingcause of pain. Chronic pain may also present in the absence of any pastinjury or evidence of body damage. Common chronic pain can includeheadache, low back pain, cancer pain, arthritis pain, neurogenic pain(pain resulting from damage to the peripheral nerves or to the centralnervous system), or psychogenic pain (pain not due to past disease orinjury or any visible sign of damage inside or outside the nervoussystem).

Chronic pain may be treated or alleviated using medications,acupuncture, surgery, and neuromodulation therapy such as localelectrical stimulation or brain stimulation, among others. Examples ofneuromodulation include Spinal Cord Stimulation (SCS), Deep BrainStimulation (DBS), Peripheral Nerve Stimulation (PNS), and FunctionalElectrical Stimulation (FES). Implantable neuromodulation systems havebeen applied to deliver such a therapy. An implantable neuromodulationsystem may include an implantable neurostimulator, also referred to asan implantable pulse generator (IPG), which can electrically stimulatetissue or nerve centers to treat nervous or muscular disorders. In anexample, an IPG can deliver electrical pulses to a specific region in apatient spinal cord, such as particular spinal nerve roots or nervebundles, to create an analgesic effect that masks pain sensation.

SUMMARY

By way of example, chronic pain management may involve determiningappropriate treatment regimens such as SCS and evaluating therapyefficacy. Accurate pain assessment and characterization are desirablefor managing patients with chronic pain. Pain may be assessed frompatient physiological or functional responses, such as sensed using oneor more sensors. A composite pain score, which characterizes patientoverall pain perception, may be generated using a combination ofsensor-based pain indicators. However, patient health status may changeover time. For example, a patient may increase or decrease their dailyexercise level, develop comorbidities, or experience worsening orimprovement of their existing chronic diseases, among other healthstatus changes. The changes in patient health condition or dailyroutines may alter the patient physiological or functional responses topain. For example, a physiological signal that used to be sensitive to,and thus more indicative of, pain intensity may become less sensitivethus less indicative of pain intensity when there are gradual changes inpatient health condition or daily routines. The present inventors haverecognized that there remains a demand for improving pain management,such as an objective, sensor-based pain assessment that can adapt tochanges in patient physiological or functional response to pain.Additionally, in an automated closed-loop pain therapy system that usespain therapy efficacy as a feedback for therapy control, the therapyefficacy may be evaluated based on sensor-based pain assessment. It isdesirable that sensors and the pain assessment mechanism be calibratedto account for changes in patient health status or changes in patientdaily routines, so as to allow for timely and individualized paintherapy titration.

This document discusses, among other things, systems, devices, andmethods for assessing pain in a subject. The system may include sensorsto sense from the patient a plurality of physiological or functionalsignals corresponding to multiple pain intensities. A pain analyzer maygenerate a pain score using the sensed physiological or functionalsignals and a fusion model. The fusion model may algorithmically combinethe sensed physiological or functional signals. The system may calibratethe fusion model based on measurements from the plurality ofphysiological or functional signals and a reference pain quantificationthat corresponds to the multiple pain intensities. The reference painquantification may be produced through a pain induction process, orderived from patient spontaneous pain episodes. The pain analyzer maygenerate a pain score using the calibrated fusion model. The system mayinclude a neurostimulator that adaptively controls delivery of paintherapy based on the pain score.

Example 1 is a system for managing pain of a patient. The systemcomprise: a sensor circuit coupled to one or more sensors configured tosense from the patient a plurality of physiological or functionalsignals corresponding to multiple pain intensities; a pain analyzercircuit coupled to the sensor circuit and configured to generate a painscore using the sensed plurality of the physiological or functionalsignals and a fusion model; a calibration circuit configured toestablish or update the fusion model based on (1) measurements from theplurality of physiological or functional signals corresponding to themultiple pain intensities and (2) a reference pain quantificationcorresponding to the multiple pain intensities; a controller circuitconfigured to control the pain analyzer circuit to generate a pain scoreusing the established or updated fusion model; and an output unitconfigured to output the pain score to a user or a process.

In Example 2, the subject matter of Example 1 optionally includes anelectrostimulator that may be configured to generate electrostimulationenergy to treat pain. The controller circuit may be configured tocontrol the electrostimulator to deliver a pain therapy and to controlthe electrostimulation energy generated by the electrostimulatoraccording to the pain score.

In Example 3, the subject matter of Example 2 optionally includes theelectrostimulator that may be further configured to deliver at least oneof: a spinal cord stimulation; a brain stimulation; or a peripheralnerve stimulation.

In Example 4, the subject matter of any one or more of Examples 2-3optionally includes the controller circuit that may be furtherconfigured to deliver first electrostimulation to the patient inresponse to the pain score exceeding a threshold value, and to deliversecond electrostimulation to the patient in response to the pain scorefalling below the threshold value. The first and secondelectrostimulations may differ in at least one of an electrostimulationenergy, an electrostimulation pulse shape, or an electrostimulationpattern.

In Example 5, the subject matter of any one or more of Examples 1-4optionally includes the calibration circuit that may be furtherconfigured to establish or update the fusion model using themeasurements of the plurality of physiological or functional signals andthe reference pain quantification during an induced pain episode.

In Example 6, the subject matter of Example 5 optionally includes theinduced pain episode that corresponds to delivery of programmedelectrostimulation to a target tissue.

In Example 7, the subject matter of any one or more of Examples 5-6optionally includes the induced pain episode that corresponds toexecution of a stress test.

In Example 8, the subject matter of any one or more of Examples 1-7optionally includes the reference pain quantification that may comprisea user input of perceived pain scales corresponding to the multiple painintensities.

In Example 9, the subject matter of any one or more of Examples 1-8optionally includes the reference pain quantification that may comprisequantified functional scores corresponding to the multiple painintensities.

In Example 10, the subject matter of any one or more of Examples 1-9optionally includes the reference pain quantification that may include aplurality of pain scales, and the calibration circuit that may beconfigured to: compute correlations between the plurality of pain scalesand measurements from the plurality of physiological or functionalsignals corresponding to the plurality of pain scales; and establish orupdate the fusion model using the computed correlations.

In Example 11, the subject matter of Example 10 optionally includes thepain analyzer circuit that may be configured to generate the pain scoreusing a combination of a plurality of signal metrics weighted by arespective plurality of weight factors. The calibration circuit may beconfigured to establish or update the fusion model by adjusting theweight factors to be proportional to the computed correlations.

In Example 12, the subject matter of any one or more of Examples 1-11optionally includes the reference pain quantification that comprises apain perception curve. The calibration circuit may be configured to:generate psychometric curves using measurements of a plurality of signalmetrics of the sensed plurality of physiological or functional signalscorresponding to the plurality of pain scales; and establish or updatethe fusion model based on an alignment metric between the painperception curve and the generated psychometric curves.

In Example 13, the subject matter of Example 12 optionally includes thepain analyzer circuit that may be configured to generate the pain scoreusing a combination of the plurality of signal metrics weighted by therespective plurality of weight factors. The calibration circuit may beconfigured to establish or update the fusion model by adjusting theweight factors to be proportional to the alignment metric between thepain perception curve and the generated psychometric curves.

In Example 14, the subject matter of any one or more of Examples 1-13optionally includes the output unit that may be further configured toproduce an alert based on the pain score.

In Example 15, the subject matter of Example 2 optionally includes animplantable neuromodulator device (IND) that includes one or more of thesensor circuit, the pain analyzer circuit, the calibration circuit, orthe electrostimulator.

Example 16 is a method for managing pain of a patient using animplantable neuromodulator device (IND). The method comprises steps of:sensing a plurality of physiological or functional signals correspondingto multiple pain intensities from a patient using one or more sensors;generating a reference pain quantification corresponding to the multiplepain intensities; establishing or updating a fusion model based on (1)measurements from the plurality of physiological or functional signalscorresponding to the multiple pain intensities and (2) the referencepain quantification corresponding to the multiple pain intensities;generating a pain score using the sensed plurality of the physiologicalor functional signals and the established or updated fusion model; andoutputting the pain score to a user or a process.

In Example 17, the subject matter of Example 16 optionally includesdelivering a pain therapy via the IND. The pain therapy may includeelectrostimulation energy determined according to the pain score.

In Example 18, the subject matter of Example 17 optionally includesdelivering a programmed electrostimulation to a target tissue to inducea pain episode with the multiple pain intensities. The fusion model maybe established or updated based on the measurements from the pluralityof physiological or functional signals during the induced pain episodeand the reference pain quantification during the induced pain episode.

In Example 19, the subject matter of Example 16 optionally includesexecuting a stress test to induce a pain episode with the multiple painintensities. The fusion model may be established or updated based on themeasurements from the plurality of physiological or functional signalsduring the induced pain episode and the reference pain quantificationduring the induced pain episode.

In Example 20, the subject matter of Example 16 optionally includes thereference pain quantification that comprises a user input of perceivedpain scales corresponding to the multiple pain intensities.

In Example 21, the subject matter of Example 16 optionally includes thereference pain quantification that comprises quantified functionalscores corresponding to the multiple pain intensities.

In Example 22, the subject matter of Example 16 optionally includes thereference pain quantification that may include a plurality of painscales, and the fusion model that may include a combination of aplurality of signal metrics weighted by a respective plurality of weightfactors. The establishing or updating the fusion model may furtherinclude establishing or updating the weight factors to be proportionalto correlations between the plurality of pain scales and measurementsfrom the plurality of physiological or functional signals correspondingto the plurality of pain scales.

In Example 23, the subject matter of Example 16 optionally includes thereference pain quantification that may include a pain perception curve,and the fusion model may include a combination of a plurality of signalmetrics weighted by a respective plurality of weight factors. Theestablishing or updating the fusion model may further include steps of:generating psychometric curves using measurements of a plurality ofsignal metrics of the sensed plurality of physiological or functionalsignals corresponding to the plurality of pain scales; and establishingor updating the weight factors to be proportional to alignment metricsbetween the pain perception curve and the generated psychometric curves.

Systems and methods of sensor-based pain assessment that adapt tochanges in patient physiological or functional response to pain, asdiscussed in this document, may improve automated patient paincharacterization, as well as individualized therapies to alleviate painor to reduce side effects. The systems, devices, and methods discussedin this document may also enhance the performance and functionality of apain management system or device. A device or a system programmed withthe sensor-based pain assessment methods can have improved automaticityin medical diagnostics. More efficient device memory or communicationbandwidth usage may be achieved by storing or transmitting medicalinformation more relevant to clinical decisions. Additionally, throughimproved pain therapy efficacy based on patient individual need, batterylongevity of an implantable device may be enhanced, or pain medicationvolume may be saved.

This summary is intended to provide an overview of subject matter of thepresent patent application. It is not intended to provide an exclusiveor exhaustive explanation of the disclosure. The detailed description isincluded to provide further information about the present patentapplication. Other aspects of the disclosure will be apparent to personsskilled in the art upon reading and understanding the following detaileddescription and viewing the drawings that form a part thereof, each ofwhich are not to be taken in a limiting sense.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are illustrated by way of example in the figures ofthe accompanying drawings. Such embodiments are demonstrative and notintended to be exhaustive or exclusive embodiments of the presentsubject matter.

FIG. 1 illustrates, by way of example and not limitation, aneuromodulation system and portions of an environment in which theneuromodulation system may operate.

FIG. 2 illustrates, by way of example and not limitation, a blockdiagram of a pain management system.

FIG. 3 illustrates, by way of example and not limitation, a blockdiagram of a pain management system comprising an implantableneuromodulator.

FIGS. 4A-B illustrate, by way of example and not limitation, blockdiagrams of portions of a pain management system for establishing orupdating a fusion model used for generating a composite pain score.

FIG. 5 illustrates, by way of example and not limitation, a referencepain curve and three psychometric curves generated during a painassessment session.

FIG. 6 illustrates, by way of example and not limitation, a flow chartof a method for managing pain in a patient.

FIG. 7 illustrates, by way of example of not limitation, a flow chart ofa method for establishing or updating a fusion model.

FIG. 8 illustrates, by way of example of not limitation, a block diagramof an example machine upon which any one or more of the techniquesdiscussed herein may perform.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof, and in which is shown byway of illustration specific embodiments in which the invention may bepracticed. These embodiments are described in sufficient detail toenable those skilled in the art to practice the invention, and it is tobe understood that the embodiments may be combined, or that otherembodiments may be utilized and that structural, logical and electricalchanges may be made without departing from the spirit and scope of thepresent invention. References to “an”, “one”, or “various” embodimentsin this disclosure are not necessarily to the same embodiment, and suchreferences contemplate more than one embodiment. The following detaileddescription provides examples, and the scope of the present invention isdefined by the appended claims and their legal equivalents.

Disclosed herein are systems, devices, and methods for or assessing painin a subject, and programming neurostimulation based on a pain scoregenerated from physiological or functional signals acquired by multiplesensors. In various embodiments, the present system may sense one ormore physiological or functional signals, and generate a pain scoreusing the sensed physiological or functional signals and a fusion model.The system may calibrate the fusion model based on measurements from thephysiological or functional signals and a reference pain quantificationcorresponding to multiple pain intensities. The system may include aneurostimulator that controls the delivery of pain therapy byautomatically adjusting stimulation parameters based on the pain scoregenerated using the calibrated fusion model.

The present system may be implemented using a combination of hardwareand software designed to provide a closed-loop pain management regimento increase therapeutic efficacy, increase patient satisfaction forneurostimulation therapies, reduce side effects, and/or increase devicelongevity. The present system may be applied in any neurostimulation(neuromodulation) therapies, including but not limited to SCS, DBS, PNS,FES, motor cortex stimulation, sacral nerve stimulation, and vagus nervestimulation (VNS) therapies. In various examples, instead of providingclosed-loop pain therapies, the systems, devices, and methods describedherein may be used to monitor the patient and assess pain that eitheroccurs intrinsically or is induced by nerve block procedures orradiofrequency ablation therapies, or side effects like paresthesiacaused by the stimulation therapy, among others. The patient monitoringmay include generating recommendations to the patient or a clinicianregarding pain treatment.

FIG. 1 illustrates, by way of example and not limitation, aneuromodulation system 100 for managing pain of a subject such as apatient with chronic pain, and portions of an environment in which theneuromodulation system 100 may operate. The neuromodulation system 100may include an implantable system 110 that may be associated with a body199 of the subject, and an external system 130 in communication with theimplantable system 110 via a communication link 120.

The implantable system 110 may include an ambulatory medical device(AMD), such as an implantable neuromodulator device (IND) 112, a leadsystem 114, and one or more electrodes 116. The IND 112 may beconfigured for subcutaneous implant in the chest, abdomen, upper glutealsurface, or other parts of the patient body 199. The IND 112 may beconfigured as a monitoring and diagnostic device. The IND 112 mayinclude a hermetically sealed can that houses sensing circuitry to sensephysiological or functional signals from the patient via sensingelectrodes or ambulatory sensors associated with the patient and incommunication with the IND 112. In some examples, the sensing electrodesor the ambulatory sensors may be included within the IND 112. Thephysiological or functional signals, when measured during a painepisode, may be correlative to severity of the pain. The IND 112 maycharacterize and quantify the pain, such as to determine onset,intensity, severity, duration, or patterns of the pain experienced bythe subject. The IND 112 may generate an alert to indicate occurrence ofa pain episode, pain exacerbation, or efficacy of pain therapy, andpresent the alert to a clinician.

The IND 112 may alternatively be configured as a therapeutic device fortreating or alleviating the pain. In addition to the pain monitoringcircuitry, the IND 112 may further include a therapy unit that cangenerate and deliver energy or modulation agents to a target tissue. Theenergy may include electrical, magnetic, thermal, or other types ofenergy. In some examples, the IND 112 may include a drug delivery systemsuch as a drug infusion pump that can deliver pain medication to thepatient, such as morphine sulfate or ziconotide, among others.

The IND 112 may include electrostimulation circuitry that generateselectrostimulation pulses to stimulate a neural target via theelectrodes 116 operably connected to the IND 112. In an example, theelectrodes 116 may be positioned on or near a spinal cord, and theelectrostimulation circuitry may be configured to deliver SCS to treatpain. In another example, the electrodes 116 may be surgically placed atother neural targets such as a brain or a peripheral neutral tissue, andthe electrostimulation circuitry may be configured to deliver brain orperipheral stimulations. Examples of electrostimulation may include deepbrain stimulation (DBS), trigeminal nerve stimulation, occipital nervestimulation, vagus nerve stimulation (VNS), sacral nerve stimulation,sphenopalatine ganglion stimulation, sympathetic nerve modulation,adrenal gland modulation, baroreceptor stimulation, or transcranialmagnetic stimulation, spinal cord stimulation (SCS), dorsal root ganglia(DRG) stimulation, motor cortex stimulation (MCS), transcranial directcurrent stimulation (tDCS), transcutaneous spinal direct currentstimulation (tsDCS), pudendal nerve stimulation, multifidus musclestimulation, transcutaneous electrical nerve stimulation (TENS), tibialnerve stimulation, among other peripheral nerve or organ stimulation.The IND 112 may additionally or alternatively provide therapies such asradiofrequency ablation (RFA), pulsed radiofrequency ablation,ultrasound therapy, high-intensity focused ultrasound (HIFU), opticalstimulation, optogenetic therapy, magnetic stimulation, other peripheraltissue stimulation therapies, other peripheral tissue denervationtherapies, or nerve blocks or injections.

In various examples, the electrodes 116 may be distributed in one ormore leads of the lead system 114 electrically coupled to the IND 112.In an example, the lead system 114 may include a directional lead thatincludes at least some segmented electrodes circumferentially disposedabout the directional lead. Two or more segmented electrodes may bedistributed along a circumference of the lead. The actual number andshape of leads and electrodes may vary according to the intendedapplication. Detailed description of construction and method ofmanufacturing percutaneous stimulation leads are disclosed in U.S. Pat.No. 8,019,439, entitled “Lead Assembly and Method of Making Same,” andU.S. Pat. No. 7,650,184, entitled “Cylindrical Multi-Contact ElectrodeLead for Neural Stimulation and Method of Making Same,” the disclosuresof which are incorporated herein by reference. The electrodes 116 mayprovide an electrically conductive contact providing for an electricalinterface between the IND 112 and tissue of the patient. Theneurostimulation pulses are each delivered from the IND 112 through aset of electrodes selected from the electrodes 116. In various examples,the neurostimulation pulses may include one or more individually definedpulses, and the set of electrodes may be individually definable by theuser for each of the individually defined pulses.

Although the discussion herein with regard to the neuromodulation system100 focuses on an implantable device such as the IND 112, this is meantonly by way of example and not limitation. It is within thecontemplation of the present inventors and within the scope of thisdocument that the systems, devices, and methods discussed herein mayalso be used for pain management via subcutaneous medical devices,wearable medical devices (e.g., wrist watch, patches, garment- orshoe-mounted device), or other external medical devices, or acombination of implantable, wearable, or other external devices. Thetherapy, such as electrostimulation or medical therapies, may be used totreat various neurological disorders other than pain, which by way ofexample and not limitation may include epilepsy, migraine, Tourette'ssyndrome, obsessive compulsive disorder, tremor, Parkinson's disease, ordystonia, among other movement and affective disorders.

The external system 130 may be communicated with the IND 112 via acommunication link 120. The external system 130 may include a dedicatedhardware/software system such as a programmer, a remote server-basedpatient management system, or alternatively a system definedpredominantly by software running on a standard personal computer. Insome examples, at least a portion of the external system 130 may beambulatory such as configured to be worn or carried by a subject. Theexternal system 130 may be configured to control the operation of theIND 112, such as to program the IND 112 for delivering neuromodulationtherapy. The external system 130 may additionally receive via thecommunication link 120 information acquired by IND 112, such as one ormore physiological or functional signals. In an example, the externalsystem 130 may generate a pain score using the physiological orfunctional signals received from the IND 112 and a fusion model. Theexternal system 130 may program the IND 112 to deliver pain therapy in aclosed-loop fashion based on the pain score. In some examples, theexternal system 130 may update the fusion model such as to adapt to thechanges in patient physiological or functional response to pain.Examples of the external system and neurostimulation based on pain scoreare discussed below, such as with reference to FIGS. 2-3.

The communication link 120 may include one or more communicationchannels and intermediate devices between the external system and theIND, such as a wired link, a telecommunication link such as an internetconnection, or a wireless link such as one or more of an inductivetelemetry link, a radio-frequency telemetry link. The communication link120 may provide for data transmission between the IND 112 and theexternal system 130. The transmitted data may include, for example,real-time physiological or functional signals acquired by and stored inthe IND 112, therapy history data, data indicating device operationalstatus of the IND 112, one or more programming instructions to the IND112 which may include configurations for sensing physiologic signal orstimulation commands and stimulation parameters, or deviceself-diagnostic test, among others. In some examples, the IND 112 may becoupled to the external system 130 further via an intermediate controldevice, such as a handheld external remote control device to remotelyinstruct the IND 112 to generate electrical stimulation pulses inaccordance with selected stimulation parameters produced by the externalsystem 130, or to store the collected data into the external system 130.

Portions of the IND 112 or the external system 130 may be implementedusing hardware, software, firmware, or combinations thereof. Portions ofthe IND 112 or the external system 130 may be implemented using anapplication-specific circuit that may be constructed or configured toperform one or more particular functions, or may be implemented using ageneral-purpose circuit that may be programmed or otherwise configuredto perform one or more particular functions. Such a general-purposecircuit may include a microprocessor or a portion thereof, amicrocontroller or a portion thereof, or a programmable logic circuit,or a portion thereof. For example, a “comparator” may include, amongother things, an electronic circuit comparator that may be constructedto perform the specific function of a comparison between two signals orthe comparator may be implemented as a portion of a general-purposecircuit that may be driven by a code instructing a portion of thegeneral-purpose circuit to perform a comparison between the two signals.

FIG. 2 illustrates, by way of example and not limitation, a blockdiagram of a pain management system 200, which may be an embodiment ofthe neuromodulation system 100. The pain management system 200 mayinclude a sensor circuit 210, a pain analyzer circuit 220, a memory 230,a user interface 240, a therapy unit 250, a calibration circuit 260, anda controller circuit 270. The pain management system 200 may beconfigured to assess patient pain using physiological or functionalsignals sensed using the sensor circuit 210 and a fusion model stored inthe memory 230.

The sensor circuit 210 may be coupled to electrodes or various types ofambulatory sensors associated with the patient to sense one or morephysiological signals from the patient. The sensor circuit 210 mayinclude sense amplifier circuit that may pre-process the sensedphysiological or functional signals, including, for example,amplification, digitization, filtering, or other signal conditioningoperations. Various physiological signals, such as cardiac, pulmonary,neural, or biochemical signals may demonstrate characteristic signalproperties in response to an onset, intensity, severity, duration, orpatterns of pain. In an example, the sensor circuit 210 may be coupledto implantable or wearable sensors to sense cardiac signals such aselectrocardiograph (ECG), intracardiac electrogram, gyrocardiography,magnetocardiography, heart rate signal, heart rate variability signal,cardiovascular pressure signal, or heart sounds signal, among others. Inanother example, the sensor circuit 210 may sense pulmonary signals suchas a respiratory signal, a thoracic impedance signal, or a respiratorysounds signal. The sensor circuit 210 may additionally or alternativelybe coupled to at least one motion sensor to sense one or more functionalsignals. The functional signal represent patient motor activities andphysical state. Examples of the functional signals may include patientposture, gait, balance, or physical activity signals, among others.Examples of the motion sensor may include an accelerometer, gyroscope(which may be a one-, two-, or three-axis gyroscope), magnetometer(e.g., a compass), inclinometers, goniometers, electromagnetic trackingsystem (ETS), or a global positioning system (GPS) sensor, among others.Detailed description of functional signals for use in paincharacterization are disclosed in commonly assigned U.S. ProvisionalPatent Application Ser. No. 62/445,075, entitled “PAIN MANAGEMENT BASEDON FUNCTIONAL MEASUREMENTS”, the disclosures of which are incorporatedherein by reference. Commonly assigned U.S. Provisional PatentApplication Ser. No. 62/445,053, entitled “PAIN MANAGEMENT BASED ONCARDIOVASCULAR PARAMETERS” describes cardiovascular parameters such asarterial pulsatile activity and electrocardiography for use in painanalysis, the disclosure of which is incorporated herein by reference inits entirety. Commonly assigned U.S. Provisional Patent Application Ser.No. 62/445,061, entitled “PAIN MANAGEMENT BASED ON BRAIN ACTIVITYMONITORING” describes information of brain activity for use in painanalysis, the disclosure of which is incorporated herein by reference inits entirety. Commonly assigned U.S. Provisional Patent Application Ser.No. 62/445,061, entitled “PAIN MANAGEMENT BASED ON BRAIN ACTIVITYMONITORING” describes information of brain activity for use in painanalysis, the disclosure of which is incorporated herein by reference inits entirety. Commonly assigned U.S. Provisional Patent Application Ser.No. 62/445,069, entitled “PAIN MANAGEMENT BASED ON RESPIRATION-MEDIATEDHEART RATES” describes information of respiration-mediated heart ratefor use in pain analysis, the disclosure of which is incorporated hereinby reference in its entirety. Commonly assigned U.S. Provisional PatentApplication Ser. No. 62/445,082, entitled “PAIN MANAGEMENT BASED ONEMOTIONAL EXPRESSION MEASUREMENTS” describes measurements of patientemotional expressions for use in pain analysis, the disclosure of whichis incorporated herein by reference in its entirety. Commonly assignedU.S. Provisional Patent Application Ser. No. 62/445,092, entitled “PAINMANAGEMENT BASED ON MUSCLE TENSION MEASUREMENTS” describes measurementsof patient muscle tension including electromyography for use in painanalysis, the disclosure of which is incorporated herein by reference inits entirety. One or more of these additional signals or measurementsmay be used by the pain analyzer circuit 220 to generate a pain score.

In some examples, the sensor circuit 210 may sense biochemical signalssuch as blood chemistry measurements or expression levels of one or morebiomarkers, which may include, by way of example and not limitation,B-type natriuretic peptide (BNP) or N-terminal pro b-type natriureticpeptide (NT-proBNP), serum cytokine profiles, P2X4 receptor expressionlevels, gamma-aminobutyric acid (GABA) levels, TNFα and otherinflammatory markers, cortisol, adenosine, Glial cell-derivedneurotrophic factor (GDNF), Nav 1.3, Nav 1.7, or Tetrahydrobiopterin(BH4) levels, among other biomarkers.

The pain analyzer circuit 220 may generate a pain score using at leastthe physiological or functional signals received from the sensor circuit210. The pain analyzer circuit 220 may be implemented as a part of amicroprocessor circuit, which may be a dedicated processor such as adigital signal processor, application specific integrated circuit(ASIC), microprocessor, or other type of processor for processinginformation including physical activity information. Alternatively, themicroprocessor circuit may be a general purpose processor that mayreceive and execute a set of instructions of performing the functions,methods, or techniques described herein.

The pain analyzer circuit 220 may include circuit sets comprising one ormore other circuits or sub-circuits that may, alone or in combination,perform the functions, methods or techniques described herein. In anexample, hardware of the circuit set may be immutably designed to carryout a specific operation (e.g., hardwired). In an example, the hardwareof the circuit set may include variably connected physical components(e.g., execution units, transistors, simple circuits, etc.) including acomputer readable medium physically modified (e.g., magnetically,electrically, moveable placement of invariant massed particles, etc.) toencode instructions of the specific operation. In connecting thephysical components, the underlying electrical properties of a hardwareconstituent are changed, for example, from an insulator to a conductoror vice versa. The instructions enable embedded hardware (e.g., theexecution units or a loading mechanism) to create members of the circuitset in hardware via the variable connections to carry out portions ofthe specific operation when in operation. Accordingly, the computerreadable medium is communicatively coupled to the other components ofthe circuit set member when the device is operating. In an example, anyof the physical components may be used in more than one member of morethan one circuit set. For example, under operation, execution units maybe used in a first circuit of a first circuit set at one point in timeand reused by a second circuit in the first circuit set, or by a thirdcircuit in a second circuit set at a different time.

As illustrated in FIG. 2, the pain analyzer circuit 220 may include asignal metrics generator 221 and a pain score generator 225. The signalmetrics generator 221 may generate one or more signal metrics from thesensed at least one physiological or functional signal. The signalmetrics may include statistical parameters extracted from the sensedsignal, such as signal mean, median, or other central tendency measuresor a histogram of the signal intensity, among others. The signal metricsmay additionally or alternatively include morphological parameters suchas maximum or minimum within a specific time period such as a cardiaccycle, positive or negative slope or higher order statistics, or signalpower spectral density at a specific frequency range, among othermorphological parameters. The signal metrics may additionally includetiming information such as a time interval between a firstcharacteristic point in one signal and a second characteristic point inanother signal.

The pain score generator 225 may generate a pain score using themeasurements of the signal metrics, such as generated by the signalmetrics generator 221, and a fusion model stored in the memory 230. Thepain score can be represented as a numerical or categorical value thatquantifies the patient overall pain symptom. The fusion model mayinvolve instructions for combining the measurements of the signalmetrics using a specific algorithm. Examples of the fusion algorithmsmay include weighted averages, voting, decision trees, or neuralnetworks, among other linear or nonlinear algorithms. In an example, thefusion model may include weighted combination of the signal metricsweighted by their respective weight factors. The combination can belinear or nonlinear. The pain score generator 225 may compare thecomposite signal metric to one or more threshold values or range values,and assign a corresponding pain score (such as numerical values from 0to 10) based on the comparison.

In another example, the pain score generator 225 may compare the signalmetrics to their respective threshold values or range values, assigncorresponding signal metric-specific pain scores based on thecomparison, and compute a composite pain score using a linear ornonlinear fusion of the signal metric-specific pain scores weighted bytheir respective weight factors. In an example, the threshold can beinversely proportional to signal metric's sensitivity to pain. A signalmetric that is more sensitive to pain may have a corresponding lowerthreshold and a larger metric-specific pain score, thus plays a moredominant role in the composite pain score than another signal metricthat is less sensitive to pain. Examples of the fusion algorithm mayinclude weighted averages, voting, decision trees, or neural networks,among others. The pain score generated by the pain score generator 225may be output to a system user or a process.

The memory 230 may be configured to store sensor signals, signalmetrics, and the pain scores such as generated by the pain scoregenerator 225. Data may be stored at the memory 230 continuously,periodically, or in a commanded mode such as triggered by a userinstruction or a specific event. As illustrated in FIG. 2, the memory230 may store the fusion model for computing the composite pain score.The fusion model may be provided by a system user, or may alternativelybe automatically established or updated such as based on thecorresponding signal metrics reliability in representing pain intensity.Examples of the automatic update of fusion model are discussed below,such as with reference to FIGS. 3 and 4.

The user interface 240 may include an input circuit 241 and an outputunit 242. In an example, at least a portion of the user interface 240may be implemented in the external system 130. The input circuit 241 mayenable a system user to program the parameters used for sensing thephysiological or functional signals, generating signal metrics, andgenerating the pain score. The input circuit 241 may be coupled to oneor more input devices such as a keyboard, on-screen keyboard, mouse,trackball, touchpad, touch-screen, or other pointing or navigatingdevices. In some example, the input device may be incorporated in amobile device such as a smart phone or other portable electronic devicethat can execute a mobile application (“App”). The mobile App may enablea patient to provide pain description or quantified pain scales duringthe pain episodes. In an example, the input circuit 241 may enable auser to confirm, reject, or edit the programming of the therapy unit250, such as parameters associated with electrostimulation, as to bediscussed as follows.

The output unit 242 may include a display to present to a system userthe pain score. The output unit 242 may also display informationincluding the physiological or functional signals, trends of the signalmetric, or any intermediary results for pain score calculation such asthe signal metric-specific pain scores. The information may be presentedin a table, a chart, a diagram, or any other types of textual, tabular,or graphical presentation formats, for displaying to a system user. Thepresentation of the output information may include audio or otherhuman-perceptible media format. In an example, the output unit 242 maygenerate alerts, alarms, emergency calls, or other forms of warnings tosignal the system user about the pain score.

The therapy circuit 250 may be configured to deliver a therapy to thepatient based on the pain score generated by the pain score generator225. The therapy circuit 250 may include an electrostimulator configuredto generate electrostimulation energy to treat pain. In an example, theelectrostimulator may deliver spinal cord stimulation (SCS) viaelectrodes electrically coupled to the electrostimulator. The electrodesmay be surgically placed at a region at or near a spinal cord tissue,which may include, by way of example and not limitation, dorsal column,dorsal horn, spinal nerve roots such as the dorsal nerve root, dorsalroot entry zone, spinothalamic tract, and dorsal root ganglia. The SCSmay be in a form of stimulation pulses that are characterized by pulseamplitude, pulse width, stimulation frequency, duration, on-off cycle,pulse shape or waveform, temporal pattern of the stimulation, amongother stimulation parameters. Examples of the stimulation pattern mayinclude burst stimulation with substantially identical inter-pulseintervals, or ramp stimulation with incremental inter-pulse intervals orwith decremental inter-pulse intervals. In some examples, the frequencyor the pulse width may change from pulse to pulse. The electrostimulatormay additionally or alternatively deliver electrostimulation to othertarget tissues such as brain or peripheral nerves tissues. In anexample, the electrostimulator may deliver transcutaneous electricalnerve stimulation (TENS) via detachable electrodes that are affixed tothe skin.

The therapy circuit 250 may additionally or alternatively include a drugdelivery system, such as an intrathecal drug delivery pump that may besurgically placed under the skin, and programmed to inject medication orbiologics through a catheter to an area around the spinal cord. Otherexamples of drug delivery system may include a computerizedpatient-controlled analgesia pump that may deliver the prescribed painmedication to the patient such as via an intravenous line. In someexamples, the therapy circuit 250 may be delivered according to the painscore received from the pain score generator 225.

The calibration circuit 260 may be configured to establish or update afusion model based on measurements from the plurality of physiologicalor functional signals corresponding to multiple pain intensities and areference pain quantification corresponding to the multiple painintensities. The reference pain quantification may be generated frompatient spontaneous pain episodes, or one or more induced pain episodesin a pain assessment session. The controller circuit 270 may control thecalibration circuit 260 to establish or update the fusion model, such asaccording to a user programming instruction, or automatically triggeredby a specific event such as a change of patient health status or dailyroutine as detected by a sensor. The controller circuit 270 mayadditionally control the pain analyzer circuit 220 to generate a painscore using the established or updated fusion model. The controllercircuit 270 may also be coupled to the therapy unit 250 to control thetherapy delivery such as electrostimulation energy according to the painscore. Examples of the calibration of fusion models are discussed below,such as with reference to FIGS. 4A-B.

FIG. 3 illustrates, by way of example and not limitation, a blockdiagram of another example of a pain management system 300, which may bean embodiment of the neuromodulation system 100 or the pain managementsystem 200. The pain management system 300 may include an implantableneuromodulator 310 and an external system 320, which may be,respectively, embodiments of the IND 112 and the external system 130 asillustrated in FIG. 1. The external system 320 may be communicativelycoupled to the implantable neuromodulator 310 via the communication link120.

The implantable neuromodulator 310 may include several components of thepain management system 200 as illustrated in FIG. 2, including thesensor circuit 210, the pain analyzer circuit 220, the memory 230, andthe therapy unit 250. As discussed with reference to FIG. 2, the painanalyzer circuit 220 includes the pain score generator 225 thatdetermine a pain score using weight factors stored in the memory 230 andthe signal metrics from the signal metrics generator 221 which may alsobe included in the pain analyzer circuit 220. In some examples, aportion or the entirety of the pain analyzer 231 may alternatively beincluded in the external system 320, or be distributed between theimplantable neuromodulator 310 and the external system 320.

The controller circuit 270 may control the generation ofelectrostimulation pulses according to specific stimulation parameters.The stimulation parameters may be provided by a system user.Alternatively, the stimulation parameters may be automaticallydetermined based on the intensity, severity, duration, or pattern ofpain, which may be subjectively described by the patient orautomatically quantified based on the physiological or functionalsignals sensed by the sensor circuit 210. For example, when apatient-described or sensor-indicated quantification exceeds arespective threshold value or falls within a specific range indicatingelevated pain, the electrostimulation energy may be increased to providestronger pain relief. Increased electrostimulation energy may beachieved by programming a higher pulse intensity, a higher frequency, ora longer stimulation duration or “on” cycle, among others. Conversely,when a patient-described or sensor-indicated pain quantification fallsbelow a respective threshold value or falls within a specific rangeindicating no pain or mild pain, the electrostimulation energy may bedecreased. The controller circuit 270 may also adjust stimulationparameters to alleviate side effects introduced by theelectrostimulation of the target tissue.

Additionally or alternatively, the controller circuit 270 may controlthe therapy unit 250 to deliver electrostimulation pulses via specificelectrodes. In an example of pain management via SCS, a plurality ofsegmented electrodes, such as the electrodes 116, may be distributed inone or more leads. The controller circuit 270 may configure the therapyunit 250 to deliver electrostimulation pulses via a set of electrodesselected from the plurality of electrodes. The electrodes may bemanually selected by a system user or automatically selected based onthe pain score.

The implantable neuromodulator 310 may receive the information aboutelectrostimulation parameters and the electrode configuration from theexternal system 320 via the communication link 120. Additionalparameters associated with operation of the therapy unit 250, such asbattery status, lead impedance and integrity, or device diagnostic ofthe implantable neuromodulator 310, may be transmitted to the externalsystem 320. The controller circuit 270 may control the generation anddelivery of electrostimulation using the information aboutelectrostimulation parameters and the electrode configuration from theexternal system 320. Examples of the electrostimulation parameters andelectrode configuration may include: temporal modulation parameters suchas pulse amplitude, pulse width, pulse rate, or burst intensity;morphological modulation parameters respectively defining one or moreportions of stimulation waveform morphology such as amplitude ofdifferent phases or pulses included in a stimulation burst; or spatialmodulation parameters such as selection of active electrodes, electrodecombinations which define the electrodes that are activated as anodes(positive), cathodes (negative), and turned off (zero), and stimulationenergy fractionalization which defines amount of current, voltage, orenergy assigned to each active electrode and thereby determines spatialdistribution of the modulation field.

In an example, the controller circuit 270 may control the generation anddelivery of electrostimulation in a closed-loop fashion by adaptivelyadjusting one or more stimulation parameters or stimulation electrodeconfiguration based on the pain score. For example, if the score exceedsthe pain threshold (or falls within a specific range indicating anelevated pain), then the first electrostimulation may be delivered.Conversely, if the composite pain score falls below a respectivethreshold value (or falls within a specific range indicating no pain ormild pain), then a second pain therapy, such as secondelectrostimulation may be delivered. The first and secondelectrostimulations may differ in at least one of the stimulationenergy, pulse amplitude, pulse width, stimulation frequency, duration,on-off cycle, pulse shape or waveform, electrostimulation pattern suchas electrode configuration or energy fractionalization among activeelectrodes, among other stimulation parameters. In an example, the firstelectrostimulation may have higher energy than the secondelectrostimulation, such as to provide stronger effect of pain relief.Examples of increased electrostimulation energy may include a higherpulse intensity, a higher frequency, or a longer stimulation duration or“on” cycle, among others.

The parameter adjustment or stimulation electrode configuration may beexecuted continuously, periodically at specific time, duration, orfrequency, or in a commanded mode upon receiving from a system user acommand or confirmation of parameter adjustment. In some examples, theclosed-loop control of the electrostimulation may be further based onthe type of the pain, such as chronic or acute pain. In an example, thepain analyzer circuit 220 may trend the signal metric over time tocompute an indication of abruptness of change of the signal metrics,such as a rate of change over a specific time period. The pain episodemay be characterized as acute pain if the signal metric changes abruptly(e.g., the rate of change of the signal metric exceeding a threshold),or as chronic pain if the signal metric changes gradually (e.g., therate of change of the signal metric falling below a threshold). Thecontroller circuit 270 may control the therapy unit 250 to deliver,withhold, or otherwise modify the pain therapy in accordance with thepain type. For example, incidents such as toe stubbing or bodilyinjuries may cause abrupt changes in certain signal metrics, but noadjustment of the closed-loop pain therapy is deemed necessary. On thecontrary, if the pain analyzer circuit 220 detects chronic paincharacterized by gradual signal metric change, then the closed-loop paintherapy may be delivered accordingly.

The external system 320 may include the user interface 240, thecalibration circuit 260, a fusion model generator 322, and a programmercircuit 324. The fusion model generator 322 may generate a fusion modelused by the pain score generator 225 to generate the pain score. In anexample, the fusion model may include a combination of signal metricsweighted by their respective weight factors indicating the signalmetrics' reliability in representing pain intensity. A sensor metricthat is more reliable, or more sensitive or specific to the pain, wouldbe assigned a larger weight than another sensor metric that is lessreliable, or less sensitive or specific to the pain. The calibrationcircuit 260 may update the fusion model using measurements signalmetrics corresponding to multiple pain intensities and a reference painquantification corresponding to the multiple pain intensities, as to bediscussed below with reference to FIGS. 4A-B.

The programmer circuit 324 may produce parameter values for operatingthe implantable neuromodulator 310, including parameters for sensingphysiological or functional signals and generating signal metrics, andparameters or electrode configurations for electrostimulation. In anexample, the programmer circuit 324 may generate the stimulationparameters or electrode configurations for SCS based on the pain scoreproduced by the pain score generator 225. Through the communication link120, the programmer circuit 324 may continuously or periodically provideadjusted stimulation parameters or electrode configuration to theimplantable neuromodulator 210. By way of non-limiting example and asillustrated in FIG. 3, the programmer circuit 324 may be coupled to theuser interface 234 to allow a user to confirm, reject, or edit thestimulation parameters, sensing parameters, or other parameterscontrolling the operation of the implantable neuromodulator 210. Theprogrammer circuit 324 may also adjust the stimulation parameter orelectrode configuration in a commanded mode upon receiving from a systemuser a command or confirmation of parameter adjustment.

The programmer circuit 324, which may be coupled to the fusion modelgenerator 322, may initiate a transmission of the weight factorsgenerated by the fusion model generator 322 to the implantableneuromodulator 310, and store the weight factors in the memory 230. Inan example, the weight factors received from the external system 320 maybe compared to previously stored weight factors in the memory 230. Thecontroller circuit 270 may update the weight factors stored in thememory 230 if the received weight factors are different than the storedweights. The pain analyzer circuit 220 may use the updated weightfactors to generate a pain score. In an example, the update of thestored weight factors may be performed continuously, periodically, or ina commanded mode upon receiving a command from a user.

In some examples, the pain score may be used by a therapy unit (such asan electrostimulator) separated from the pain management system 300. Invarious examples, the pain management system 300 may be configured as amonitoring system for pain characterization and quantification withoutdelivering closed-loop electrostimulation or other modalities of paintherapy. The pain characterization and quantification may be provided toa system user such as the patient or a clinician, or to a processincluding, for example, an instance of a computer program executable ina microprocessor. In an example, the process includescomputer-implemented generation of recommendations or an alert to thesystem user regarding pain medication (e.g., medication dosage and timefor taking a dose), electrostimulation therapy, or other pain managementregimes. The therapy recommendations or alert may be based on the painscore, and may be presented to the patient or the clinician in varioussettings including in-office assessments (e.g. spinal cord stimulationprogramming optimization), in-hospital monitoring (e.g. opioid dosingduring surgery), or ambulatory monitoring (e.g. pharmaceutical dosingrecommendations).

In an example, in response to the pain score exceeding a threshold whichindicates elevated pain symptom, an alert may be generated and presentedat the user interface 240 to remind the patient to take pain medication.In another example, therapy recommendations or alerts may be based oninformation about wearing-off effect of pain medication, which may bestored in the memory 230 or received from the user interface 240. Whenthe drug effect has worn off, an alert may be generated to remind thepatient to take another dose or to request a clinician review of thepain prescription. In yet another example, before a pain therapy such asneurostimulation therapy is adjusted (such as based on the pain score)and delivered to the patient, an alert may be generated to forewarn thepatient or the clinician of any impending adverse events. This may beuseful as some pain medication may have fatal or debilitating sideeffects. In some examples, the pain management system 300 may identifyeffect of pain medication addiction such as based on physiological orfunctional signals. An alert may be generated to warn the patient abouteffects of medication addiction and thus allow medical intervention.

In some examples, the pain analyzer circuit 220 may be alternativelyincluded in the external system 320. The pain analyzer circuit 220, or aportion of the pain analyzer circuit 220 such as the signal metricsgenerator 221 or the pain score generator 225, may be included in awearable device configured to be worn or carried by a subject. At leasta portion of the sensor circuit 210 may also be included in the externalsystem 320. A clinician may use the external system 320 to program theimplantable neuromodulator 310 with appropriate pain therapy based onthe pain score generated at the external system 320, such as during aclinical trial or patient follow-up visit at the clinic.

FIGS. 4A-B illustrate, by way of example and not limitation, blockdiagrams of portions of a pain management system for establishing orupdating a fusion model used for generating a composite pain score. Thesystem portions in FIGS. 4A-B include respective calibration circuits460A-B, which may be embodiments of the calibration circuit 260. Thecalibration circuits 460A-B may each be coupled to a sensor circuit 210,and one or both of the user interface 240 or a functional analyzer 420.During one or more pain episodes that involve multiple levels of painintensities, the calibration circuits 460A-B may each determine areference pain quantification corresponding to the multiple painintensities using one or both of the user interface 240 or a functionalanalyzer 420, sense a plurality of physiological or functional signalscorresponding to the multiple pain intensities using the sensor circuit210, and calibrate the fusion model such as based on a comparison of thereference pain quantification and the plurality of physiological orfunctional signals.

As illustrated in each of FIGS. 4A-B, the calibration circuit may becoupled to a pain episodes inducer/receiver 450 that may receivespontaneous pain episodes or induce pain episodes. The spontaneous painepisodes may occur in an ambulatory setting in patient daily life. Uponan onset of a spontaneous pain episode, the sensor circuit 210 mayrecord a plurality of physiological or functional signals, automaticallyor activated at least partially by the patient. The induced painepisodes may be produced in a pain assessment session administered by aclinician. An external stimulator or an implantable stimulator (such asthe implantable neuromodulator 310) may be programmed, such as by aclinician during a patient follow-up, to execute a pain assessmentprotocol that includes different levels of stimulation energy. Thedifferent stimulation energy levels may be achieved by adjusting thepulse intensity, duration, frequency, on/off period, or electrodeselection and stimulation vector configuration, among other therapyparameters. The pain assessment session may include a low stimulationenergy level such as by temporarily withholding delivery of pain-reliefelectrostimulation, a high stimulation energy level such as bydelivering the maximal tolerable and safe pain-relief stimulationprescribed by the clinician, or one or more intermediate stimulationenergy levels between the minimal and maximal energy levels to achieveintermediate levels of pain reduction effect. Electrostimulation withdifferent levels of stimulation energy may result in different painintensities. Additionally or alternatively, the pain assessment protocolmay include pressure stimulation, thermal stimulation (e.g., hot or coldstimulation applied to patient skin), or other peripheral somatosensorystimulation. In some examples, the pain assessment protocol may includenon-pain related tasks, such as stress, leg lift, or grip test. Thesensor circuit 210 may record a plurality of physiological or functionalsignals during the pain assessment session.

The signal metrics generator 221 may generate, from the plurality ofphysiological or functional signals sensed during the spontaneous orinduced pain episodes, a set of signal metrics {X}. For multiplespontaneous or induced pain episodes with multiple such as a total of npain intensities (P1, P2, . . . , Pn), the signal metrics generator 221may generate corresponding multiple sets of signal metrics 464 ({X1},{X2}, . . . , {Xn}), where each signal metric set {Xi}={Xi(1), Xi(2), .. . , Xi(m)} represents m signal metrics corresponding to thespontaneous or induced pain episode with pain intensity level of Pi.Also during the spontaneous or induced pain episodes, the patient mayprovide, via the user interface 240, self-reported perceived pain scales462, denoted by (rP1, rP2, . . . , rPn) that correspond to the n painintensities. The patient self-reported perceived pain scales (rP1, rP2,. . . , rPn) may take numerical or categorical values, and represent areference pain quantification corresponding to the multiple painintensities.

The functional analyzer 420 may alternatively or additionally performquantified functional assessment of the patient, and generate functionalscores 463, denoted by (F1, F2, . . . , Fn), that correspond to themultiple pain intensities during the spontaneous or induced painepisodes. The functional scores 463 represent patient motion controlfunctionality such as a posture, a gait, a balance while in locomotion,a locomotion pattern, or a physical activity level. The functionalscores 463 may be generated when the patient undergoes a standardfunctional assessment test, such as one or more of a gait analysisprocedure, a six-minute walk test, or a timed up-and-go test, amongother standardized functional tests. The gait analysis procedureevaluates a patient endurance or fatigue during locomotion. Thesix-minute walk test measures the distance an individual is able to walkover a total of six minutes on a hard, flat surface, and is an indicatorof a patient functional exercise capacity. The timed up-and-go testmeasures the time that a person takes to rise from a chair, walk threemeters, turn around, walk back to the chair, and sit down, and is anindicator of a patient mobility.

The functional scores (F1, F2, . . . , Fn) may be indicative of variouslevels of pain intensities. For example, with elevated pain, the patientmay present with significantly unbalanced posture and abnormal gait orlocomotion patterns, shorter six-minute walk distance, or longer timefor completion of the timed up-and-go test. The functional scores (F1,F2, . . . , Fn) represent a reference pain quantification correspondingto the multiple pain intensities. A correspondence between thefunctional scores and the patient pain at different pain intensities maybe specified and stored in the device memory 230.

The calibration circuit 460A-B may each compare the signal metrics 464generated from the physiological or functional signals to the referencepain quantification such as one or both of the pain scales 462 or thefunctional scores 463. FIG. 4A illustrates a block diagram of thecalibration circuit 460A that includes a correlator 465 that maycalculate a correlation between the signal metrics 464 corresponding ton pain intensities (P1, P2, . . . , Pn), and the pain scale 462corresponding to the same n pain intensities (P1, P2, . . . , Pn). Forexample, for signal metric p (1≤p≤m), the correlation may be representedby corr{(X1(p), X2(p), . . . , Xn(p)), (rP1, rP2, . . . , rPn)}.Additionally or alternatively, the correlator 465 may calculate acorrelation between the signal metrics 464 and the functional scores 463each corresponding to n pain intensities (P1, P2, . . . , Pn). Forexample, for signal metric p (1≤p≤m), the correlation may be representedby corr{(X1(p), X2(p), . . . , Xn(p)), (F1, F2, . . . , Fn)}. Becausethe functional scores (F1, F2, . . . , Fn) correlate to the patientperceived pain intensities, the correlation between the signal metrics464 and the functional scores 462 indirectly indicate the correlationsbetween the signal metrics 412 and the patient perceived painintensities. It is recognized that in some patients such as those withspeech or mental disorders, acquiring patient subjective paindescription or patient self-reported pain scales 462 may not befeasible. The correlations between the signal metrics 464 and thepatient self-reported pain scales 462, corr{(X1(p), X2(p), . . . ,Xn(p)), (rP1, rP2, . . . , rPn)}, may be more likely subject to inter-or intra-patient variation and therefore not reliable. Comparatively,the correlations between the signal metrics 464 and the functionalscores 463, corr{(X1(p), X2(p), . . . , Xn(p)), (F1, F2, . . . , Fn)},may be a more feasible measure in these patients for establishing orupdating the fusion model.

In some examples, the correlator 465 may perform regression analysis anddetermine a regression line or curve that fits the data. The slope ortrend of the fitted line or curve may indicate the sensitivity of thesignal metric to pain. The fusion model generator 322 may generate orupdate the fusion model, such as by assigning weight factors for thesignal metrics, based on the calculated correlation. In an example, theweight factors may be proportional to the correlations.

In some examples, the correlator 465 may use both the correlationbetween the signal metrics 464 and the pain scale 462, and thecorrelation between the signal metrics 464 and the functional scores463, to determine or adjust the fusion model. In an example, between twosignal metrics X(a) and X(b) that correlate almost equally well with thepatient self-reported pain scales 462, if the functional scores 463correlates with X(a) more closely than with X(b), then the fusion modelmay include a greater weight factor for X(a) that a weight factor forX(b).

FIG. 4B illustrates a block diagram of the calibration circuit 460B thatincludes a reference pain curve generator 466, a psychometric curvegenerator 467, and a comparator 468. The reference pain curve generator466, which receives reference pain 461 such as the pain scales 462 orthe functional scores 462 as input, may generate a reference pain curvethat represents patient-reported pain intensities or functional scoresat various pain intensities. The psychometric curve generator 467 mayreceive signal metrics 464 as input and generate one or morepsychometric curves {Cx(1), Cx(2), . . . , Cx(m)} corresponding to therespective m signal metrics. The psychometric curves represent patientphysiological or functional responses (as indicated by the respectivesignal metrics) at various pain intensities. In various examples, thereference pain curve generator 466 and the psychometric curve generator467 may each perform curve smoothing, regression, interpolation, orextrapolation, among other curve fitting procedures. The reference paincurve and the one or more psychometric curves {Cx(1), Cx(2), . . . ,Cx(m)} may be graphically displayed on a screen such as on the outputunit 242. Examples of the reference pain curve and psychometric curvescorresponding to various pain intensities are discussed below, such aswith reference to FIG. 5.

The comparator 468 may compare the reference pain curve and each of thepsychometric curves to determine an alignment metric indicatingmorphological similarity between the reference pain curve and each ofthe psychometric curves. Examples of the alignment metric may includemulti-dimensional distance measures such as a mean-squared error,distance in a normed vector space (such as L1 norm, L2 norm or Euclidiandistance, and infinite norm), correlation coefficient, mutualinformation, or ratio image uniformity, among others. The fusion modelgenerator 322 may use the alignment metric to establish or adjust thefusion model. In an example, the fusion model generator 322 maydetermine weight factors for the signal metrics to be proportional tothe alignment metrics between the pain perception curve and thegenerated psychometric curves.

FIG. 5 illustrates, by way of example and not limitation, a referencepain curve 510 and three psychometric curves 521-523, which may berespectively generated by the pain perception cure generator 466 and thepsychometric curve generator 467 of the calibration circuit 460B. Thehorizontal axis 530 represents various pain intensities, such as energylevels of a pain-induction stimulation or dosage of pain-inductionagents applied in a pain assessment session, or various pain intensitiescorresponding to spontaneous pain episodes. The vertical axis 540represents reference pain or patient physiological or functionalresponses to pain. The reference pain curve 510 depicts patient-reportedpain intensities or functional scores varying with various painintensities. The psychometric curves 521-523 depict measurements ofrespective signal metrics corresponding to various pain intensities. Inthe example as illustrated in FIG. 5, the reference pain curve 510 hasan “S” shape, indicating the patient self-reported pain perception orthe functional score is a sigmoid-type of function of the intensities ofspontaneous or induced pain episodes. Among the illustrativepsychometric curves 521-523, the curve 522 has a similar “S” shapecomprising both a concave and a convex portion within the range of thepain intensity parameter tested and displayed (as shown in thehorizontal axis 530). The psychometric curve 521 has a convex shape, andthe psychometric curve 523 has a concave shape, neither of which issimilar to the “S”-shaped reference pain curve 510. The comparator 468may compute alignment metrics, such as Euclidean distances, between thereference pain curve 510 and each of the psychometric curves 521-523.The psychometric curve 522 is morphologically more aligned with thereference pain curve 510. The fusion model generator may accordinglygenerate or adjust a fusion model such as by assigning a larger weightfactor to the signal metric associated with the psychometric curve 522,than to the signal metrics associated with the psychometric curves 521or 523.

FIG. 6 illustrates, by way of example and not limitation, a method 600for managing pain in a patient. The method 600 may be implemented in amedical system, such as the pain management system 200 or 300. In anexample, at least a portion of the method 600 may be executed by aneuromodulator device (IND) such as the implantable neuromodulator 310.In an example, at least a portion of the method 600 may be executed byan external programmer or remote server-based patient management system,such as the external system 320 communicatively coupled to the IND 310.The method 600 may be used to provide neuromodulation therapy to treatchronic pain or other disorders.

The method 600 begins at step 610, where a plurality of physiological orfunctional signals may be sensed such as via electrodes or ambulatorysensors associated with the patient. Examples of the physiologicalsignals may include cardiac, pulmonary, or neural signals, such as, byway of example of limitation, electrocardiograph (ECG) or intracardiacelectrogram, heart rate signal, heart rate variability signal,cardiovascular pressure signal, or heart sounds signal, respiratorysignal, a thoracic impedance signal, or a respiratory sounds signal, orneural activity signal. The physiological signals may also include bloodchemistry measurements or biomarkers that are indicative of onset,intensity, severity, duration, or different patterns of pain. In someexamples, more functional signals may additionally be sensed at 610.Examples of the functional signals may include, for example, patientposture, gait, balance, or physical activity signals, among others. Thefunctional signals may responsively co-variate with a pain episode.

At 620, a reference pain quantification corresponding to multiple painintensities may be generated. The reference pain quantification may begenerated during spontaneous pain episodes occurred in an ambulatorysetting in patient daily life, or generated during induced pain episodeswhen a clinician executes a pain assessment session in a clinic. Thespontaneous pain episodes may trigger recording of a plurality ofphysiological or functional signals, automatically or activated at leastpartially by the patient. The induction of pain episodes with variouspain intensities may be generated by executing a pain assessmentprotocol, which may include electrostimulation or a stress test toinduce paint with various pain intensities.

At 630, a fusion model may be established or updated. The fusion modelmay be used to algorithmically combine the sensed physiological orfunctional signals to determine an objective pain score. The fusionmodel may be established or calibrated based on measurements from theplurality of physiological or functional signals and the reference painquantification corresponding to multiple pain intensities. During thespontaneous or induced pain episode, one or more physiological orfunctional signals may be sensed, and a plurality of signal metrics maybe generated from the sensed physiological or functional signals, suchas via the signal metrics generator 221. The signal metrics may includestatistical parameters, morphological parameters, or temporalparameters. The fusion model may involve instructions for combining themeasurements of the signal metrics using a specific algorithm. Examplesof the fusion algorithms may include weighted averages, voting, decisiontrees, or neural networks, among other linear or nonlinear algorithms.In an example, the fusion model may include weighted combination of thesignal metrics weighted by their respective weight factors. Thecombination can be linear or nonlinear.

For multiple spontaneous or induced pain episodes with multiple painintensities, a set of signal metrics may be measured corresponding toeach of the plurality of pain intensities. Also during the spontaneousor induced pain episode, reference pain quantification may also begenerated. The physiological or functional signal metrics may becompared to the reference pain quantification, such as one or both ofthe pain scales and the functional scores during spontaneous or inducedpain episodes, and determine or update the structure or one or moreparameters of the fusion model based on the comparison. Examples of thefusion model establishment or update are discussed below such as withreference to FIG. 7.

At 640, a pain score may be generated using the measurements of thesignal metrics and the fusion model. The pain score may be representedas a numerical or categorical value that quantifies overall pain qualityin the subject. In an example, the fusion model includes a weightedcombination of signal metrics. A composite signal metric may begenerated using a combination of the signal metrics weighted by theirrespective weight factors. The composite signal metric may becategorized as one of a number of degrees of pain by comparing thecomposite signal metric to one or more threshold values or range values,and a corresponding pain score may be assigned based on the comparison.In another example, the signal metrics may be compared to theirrespective threshold values or range values and a corresponding signalmetric-specific pain score may be determined. A composite pain score maybe generated using a linear or nonlinear fusion of the signalmetric-specific pain scores each weighted by their respective weightfactors.

At 642, the pain score may be output to a user or to a process, such asvia the output unit 242 as illustrated in FIG. 2. The pain score,including the composite pain score and optionally together withmetric-specific pain scores, may be displayed on a display screen. Otherinformation such as the physiological or functional signals and thesignal metrics may also be output for display or for further processing.In some examples, alerts, alarms, emergency calls, or other forms ofwarnings may be generated to signal the system user about occurrence ofa pain episode or aggravation of pain as indicated by the pain score.

The method 600 may include, at 644, an additional step of delivering apain therapy to the patient according to the pain score. The paintherapy may include electrostimulation therapy, such as spinal cordstimulation (SCS) via electrodes electrically coupled to theelectrostimulator. The SCS may be in a form of stimulation pulses thatare characterized by pulse amplitude, pulse width, stimulationfrequency, duration, on-off cycle, waveform, among other stimulationparameters. Other electrostimulation therapy, such as one or acombination of DBS, FES, VNS, TNS, or PNS at various locations, may bedelivered for pain management. The pain therapy may additionally oralternatively include a drug therapy such as delivered by using anintrathecal drug delivery pump.

In various examples, the pain therapy (such as in the form ofelectrostimulation or drug therapy) may be delivered in a closed-loopfashion. Therapy parameters, such as stimulation waveform parameters,stimulation electrode combination and fractionalization, drug dosage,may be adaptively adjusted based at least on the pain score. Thepain-relief effect of the delivered pain therapy may be assessed basedon the signal metrics such as the cardiovascular parameters, and thetherapy may be adjusted to achieve desirable pain relief. The therapyadjustment may be executed continuously, periodically at specific time,duration, or frequency, or in a commanded mode upon receiving from asystem user a command or confirmation of parameter adjustment. In anexample, if the pain score exceeds the pain threshold (or falls within aspecific range indicating an elevated pain), then the firstelectrostimulation may be delivered. Conversely, if the composite painscore falls below a respective threshold value (or falls within aspecific range indicating no pain or mild pain), then a second paintherapy, such as second electrostimulation may be delivered. The firstand second electrostimulations may differ in at least one of thestimulation energy, pulse amplitude, pulse width, stimulation frequency,duration, on-off cycle, pulse shape or waveform, electrostimulationpattern such as electrode configuration or energy fractionalizationamong active electrodes, among other stimulation parameters. The method600 may proceed at 610 to sense physiological or functional signals inresponse to the therapy delivered at 644. In some examples, theresponses of the signal metrics to pain therapy delivered at 644 may beused to adjust the fusion model such as by adjusting the weight factorsfor the signal metrics.

FIG. 7 illustrates, by way of example of not limitation, a diagram of amethod 730 for establishing or updating a fusion model using a pluralityof physiological or functional signals and a reference painquantification corresponding to the multiple pain intensities. Themethod 730 may be an embodiment of the steps of generating the referencepain quantification 620 and establishing or updating the fusion model630 as illustrated in method 600.

The method 730 begins at 731 with inducing pain episodes. The paininduction may involve delivering different levels of stimulation energyaccording to a pain assessment protocol via an external stimulator orthe implantable neuromodulator 310 to induce pain. The pain assessmentprotocol may include electrostimulation with different levels ofstimulation energy, which may result in different pain intensities. Inan example, the pain assessment protocol may include trains ofelectrostimulation at different levels of stimulation energy for paintherapy, such as during a patient follow-up visit in a clinic. Thedifferent stimulation energy may be achieved by adjusting the pulseintensity, duration, frequency, on/off period, or electrode selectionand stimulation vector configuration, among other therapy parameters. Inan example, the pain assessment session may include a low stimulationenergy level such as by temporarily withholding delivery of pain-reliefelectrostimulation, a high stimulation energy level such as bydelivering the maximal tolerable and safe pain-relief stimulation asprescribed by the clinician, and optionally one or more intermediatestimulation energy levels between the minimal and maximal energy levelsto achieve intermediate levels of pain reduction effect. A patient withchronic pain may experience various degrees of pain symptomscorresponding to the stimulation energy levels. The pain assessmentprotocol may include respective durations for each stimulation energylevel, such as to allow patient adaptation to changes of stimulationenergy from one level to another, and to allow stabilization of patientphysiological or functional responses and pain sensation. Thestimulation energy levels may be arranged in a ramp-up, a ramp-down, anintermittent, or a random order. The pain assessment protocol mayadditionally or alternatively involve pressure, thermal, or otherperipheral somatosensory stimulations, a stress test, or other non-painrelated tasks.

At 732, a reference pain quantification may be generated. The referencepain quantification may be generated during the pain episodes using thecalibration circuit 460A or 460B as illustrated in FIGS. 4A-B. In anexample, the reference pain quantification may include patientself-reported perceived pain scales corresponding to the multiple painintensities during the spontaneous or induced pain episodes. The patientself-reported perceived pain scales may take numerical or categoricalvalues. In some examples, the user input may include patient qualitativepain description such as a pain drawing or a patient questionnaire. Thequalitative pain description may be transformed to pain scales, such asa discrete or continuous numeric value. In another example, thereference pain quantification may include functional scorescorresponding to the multiple pain intensities during the spontaneous orinduced pain episodes. The functional scores represent patient motioncontrol functionality such as a posture, a gait, a balance while inlocomotion, a locomotion pattern, or a physical activity level. Thepatient may undergo a standard functional assessment test, such as oneor more of a gait analysis procedure, a six-minute walk test, or a timedup-and-go test, among other standardized tests, and functional scoresmay be obtained from the standardized tests. The functional scoresindicative various levels of pain intensities. The patient with chronicpain may present with significantly unbalanced posture and abnormal gaitor locomotion patterns, shorter six-minute walk distance, or longer timefor completion of the timed up-and-go test.

At 733, a plurality of signal metrics may be measured during the inducedpain episodes. The signal metrics may be generated from physiological orfunctional signals that are sensed during the induced pain episodes. At734, correlations between the reference pain quantification and thesignal metrics corresponding to various pain intensities may becomputed, such as using the correlator 465. In an example, thecorrelations are evaluated between the signal metrics corresponding tomultiple pain intensities and the patient self-reported pain scalecorresponding to the same multiple pain intensities. In another example,the correlations are evaluated between the signal metrics correspondingto multiple pain intensities and the functional scores corresponding tothe same multiple pain intensities. The signal metric measurements andthe pain scales may be graphically presented to a system user such as tobe displayed in the user interface 234. A regression analysis may beperformed to determine a regression line or curve that fits the painscales and the signal metric measurements during the induced painepisodes. The correlations may be graphically represented by thespreadness of the signal metric measurements with respect to theregression line. The slope or trend of the fitted line or curve mayindicate the sensitivity of the signal metric to the pain. Then, at 738,a fusion model may be generated, or an existing fusion model may beupdated, based on the calculated correlation. In an example, the fusionmodel may include a linear or a nonlinear combination of signal metricsweighted by their respective weight factors. The fusion model may beupdated by assigning weight factors for the signal metrics based on thecalculated correlation. In an example, the weight factor for a signalmetric is proportional to the correlation between the reference painquantification and the signal metric.

In addition to or in lieu of establishing or updating the fusion modelbased on the correlations between reference pain quantification andsignal metrics such as obtained at 734, the fusion model may beestablished or updated based on similarity between patient painperception and physiological or functional response to pain. Asillustrated in FIG. 7, at 735 a reference pain curve may be generated,such as using the reference pain curve generator 466. The reference paincurve, such as the curve 510 in FIG. 5, represents patient-reported painintensities, or functional scores, at various pain intensities. At 736,one or more psychometric curves may be generated for the respectivesignal metrics. The psychometric curves, such as the curve 521-523 inFIG. 5, represent patient physiological or functional responses (asindicated by the respective signal metrics) at various pain intensities.The reference pain curve and the one or more psychometric curves mayeach be processed including curve smoothing, regression, interpolation,or extrapolation. At 737, an alignment metric between the reference paincurve and each of the psychometric curves may be computed. The alignmentmetric indicates a degree of morphological similarity between thereference pain curve and each of the psychometric curves. The alignmentmetric may be computed as a multi-dimensional distance measures such asa mean-squared error, distance in a normed vector space, correlationcoefficient, mutual information, or ratio image uniformity, amongothers. Then, at 738, a fusion model may be generated, or an existingfusion model may be updated, based on the curve alignment metric. In anexample, the fusion model may include weighted combination of the signalmetrics weighted by their respective weight factors. The fusion modelmay be updated by assigning weight factors for the signal metrics basedon the respective alignment metrics. In an example, the weight factorsfor a signal metric is proportional to the alignment metric between thereference pain curve and the psychometric curve corresponding to thesignal metric.

FIG. 8 illustrates generally a block diagram of an example machine 800upon which any one or more of the techniques (e.g., methodologies)discussed herein may perform. Portions of this description may apply tothe computing framework of various portions of the LCP device, the IND,or the external programmer.

In alternative embodiments, the machine 800 may operate as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine 800 may operate in the capacity of aserver machine, a client machine, or both in server-client networkenvironments. In an example, the machine 800 may act as a peer machinein peer-to-peer (P2P) (or other distributed) network environment. Themachine 800 may be a personal computer (PC), a tablet PC, a set-top box(STB), a personal digital assistant (PDA), a mobile telephone, a webappliance, a network router, switch or bridge, or any machine capable ofexecuting instructions (sequential or otherwise) that specify actions tobe taken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein, such as cloud computing, software as aservice (SaaS), other computer cluster configurations.

Examples, as described herein, may include, or may operate by, logic ora number of components, or mechanisms. Circuit sets are a collection ofcircuits implemented in tangible entities that include hardware (e.g.,simple circuits, gates, logic, etc.). Circuit set membership may beflexible over time and underlying hardware variability. Circuit setsinclude members that may, alone or in combination, perform specifiedoperations when operating. In an example, hardware of the circuit setmay be immutably designed to carry out a specific operation (e.g.,hardwired). In an example, the hardware of the circuit set may includevariably connected physical components (e.g., execution units,transistors, simple circuits, etc.) including a computer readable mediumphysically modified (e.g., magnetically, electrically, moveableplacement of invariant massed particles, etc.) to encode instructions ofthe specific operation. In connecting the physical components, theunderlying electrical properties of a hardware constituent are changed,for example, from an insulator to a conductor or vice versa. Theinstructions enable embedded hardware (e.g., the execution units or aloading mechanism) to create members of the circuit set in hardware viathe variable connections to carry out portions of the specific operationwhen in operation. Accordingly, the computer readable medium iscommunicatively coupled to the other components of the circuit setmember when the device is operating. In an example, any of the physicalcomponents may be used in more than one member of more than one circuitset. For example, under operation, execution units may be used in afirst circuit of a first circuit set at one point in time and reused bya second circuit in the first circuit set, or by a third circuit in asecond circuit set at a different time.

Machine (e.g., computer system) 800 may include a hardware processor 802(e.g., a central processing unit (CPU), a graphics processing unit(GPU), a hardware processor core, or any combination thereof), a mainmemory 804 and a static memory 806, some or all of which may communicatewith each other via an interlink (e.g., bus) 808. The machine 800 mayfurther include a display unit 810 (e.g., a raster display, vectordisplay, holographic display, etc.), an alphanumeric input device 812(e.g., a keyboard), and a user interface (UI) navigation device 814(e.g., a mouse). In an example, the display unit 810, input device 812and UI navigation device 814 may be a touch screen display. The machine800 may additionally include a storage device (e.g., drive unit) 816, asignal generation device 818 (e.g., a speaker), a network interfacedevice 820, and one or more sensors 821, such as a global positioningsystem (GPS) sensor, compass, accelerometer, or other sensor. Themachine 800 may include an output controller 828, such as a serial(e.g., universal serial bus (USB), parallel, or other wired or wireless(e.g., infrared (IR), near field communication (NFC), etc.) connectionto communicate or control one or more peripheral devices (e.g., aprinter, card reader, etc.).

The storage device 816 may include a machine readable medium 822 onwhich is stored one or more sets of data structures or instructions 824(e.g., software) embodying or utilized by any one or more of thetechniques or functions described herein. The instructions 824 may alsoreside, completely or at least partially, within the main memory 804,within static memory 806, or within the hardware processor 802 duringexecution thereof by the machine 800. In an example, one or anycombination of the hardware processor 802, the main memory 804, thestatic memory 806, or the storage device 816 may constitute machinereadable media.

While the machine readable medium 822 is illustrated as a single medium,the term “machine readable medium” may include a single medium ormultiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) configured to store the one or moreinstructions 824.

The term “machine readable medium” may include any medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine 800 and that cause the machine 800 to perform any one ormore of the techniques of the present disclosure, or that is capable ofstoring, encoding or carrying data structures used by or associated withsuch instructions. Non-limiting machine readable medium examples mayinclude solid-state memories, and optical and magnetic media. In anexample, a massed machine readable medium comprises a machine readablemedium with a plurality of particles having invariant (e.g., rest) mass.Accordingly, massed machine-readable media are not transitorypropagating signals. Specific examples of massed machine readable mediamay include: non-volatile memory, such as semiconductor memory devices(e.g., Electrically Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM)) and flash memorydevices; magnetic disks, such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 824 may further be transmitted or received over acommunications network 826 using a transmission medium via the networkinterface device 820 utilizing any one of a number of transfer protocols(e.g., frame relay, internet protocol (IP), transmission controlprotocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP), etc.). Example communication networks may include alocal area network (LAN), a wide area network (WAN), a packet datanetwork (e.g., the Internet), mobile telephone networks (e.g., cellularnetworks), Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., Institute of Electrical and Electronics Engineers (IEEE)802.11 family of standards known as WiFi®, IEEE 802.16 family ofstandards known as WiMax®), IEEE 802.15.4 family of standards,peer-to-peer (P2P) networks, among others. In an example, the networkinterface device 820 may include one or more physical jacks (e.g.,Ethernet, coaxial, or phone jacks) or one or more antennas to connect tothe communications network 826. In an example, the network interfacedevice 820 may include a plurality of antennas to wirelessly communicateusing at least one of single-input multiple-output (SIMO),multiple-input multiple-output (MIMO), or multiple-input single-output(MISO) techniques. The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions for execution by the machine 800, and includesdigital or analog communications signals or other intangible medium tofacilitate communication of such software.

Various embodiments are illustrated in the figures above. One or morefeatures from one or more of these embodiments may be combined to formother embodiments.

The method examples described herein can be machine orcomputer-implemented at least in part. Some examples may include acomputer-readable medium or machine-readable medium encoded withinstructions operable to configure an electronic device or system toperform methods as described in the above examples. An implementation ofsuch methods may include code, such as microcode, assembly languagecode, a higher-level language code, or the like. Such code may includecomputer readable instructions for performing various methods. The codecan form portions of computer program products. Further, the code can betangibly stored on one or more volatile or non-volatilecomputer-readable media during execution or at other times.

The above detailed description is intended to be illustrative, and notrestrictive. The scope of the disclosure should, therefore, bedetermined with references to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A system for managing pain of a patient, thesystem comprising: a sensor circuit coupled to one or more sensorsconfigured to sense from the patient a plurality of physiological orfunctional signals corresponding to multiple pain intensities; a painanalyzer circuit coupled to the sensor circuit and configured togenerate a pain score using a fusion model that combines the sensedplurality of physiological or functional signals; a calibration circuitconfigured to establish or update the fusion model based on acorrelation between (1) measurements from the plurality of physiologicalor functional signals corresponding to the multiple pain intensities and(2) a reference pain quantification corresponding to the multiple painintensities; a controller circuit configured to control the painanalyzer circuit to generate a pain score using the established orupdated fusion model; and an output unit configured to output the painscore to a user or a process.
 2. The system of claim 1, furthercomprising an electrostimulator configured to generateelectrostimulation energy to treat pain, wherein the controller circuitis configured to control the electrostimulator to deliver a pain therapyand to control the electrostimulation energy generated by theelectrostimulator according to the pain score.
 3. The system of claim 2,wherein the electrostimulator is further configured to deliver at leastone of: a spinal cord stimulation; a brain stimulation; or a peripheralnerve stimulation.
 4. The system of claim 2, further comprising animplantable neuromodulator device (IND) that includes one or more of thesensor circuit, the pain analyzer circuit, the calibration circuit, orthe electrostimulator.
 5. The system of claim 1, wherein the calibrationcircuit is further configured to establish or update the fusion modelduring an induced pain episode with the multiple pain intensitiescorresponding to delivery of programmed electrostimulation to a targettissue.
 6. The system of claim 1, wherein the calibration circuit isfurther configured to establish or update the fusion model during aninduced pain episode with the multiple pain intensities corresponding toexecution of a stress test.
 7. The system of claim 1, wherein thereference pain quantification comprises a user input of perceived painscales corresponding to the multiple pain intensities.
 8. The system ofclaim 1, wherein the reference pain quantification comprises quantifiedfunctional scores corresponding to the multiple pain intensities.
 9. Thesystem of claim 1, wherein the reference pain quantification includes aplurality of pain scales, and wherein the calibration circuit isconfigured to: compute correlations between the plurality of pain scalesand measurements from the plurality of physiological or functionalsignals corresponding to the plurality of pain scales; and establish orupdate the fusion model using the computed correlations.
 10. The systemof claim 9, wherein: the pain analyzer circuit is configured to generatethe pain score using a combination of a plurality of signal metricsweighted by a respective plurality of weight factors; and thecalibration circuit is configured to establish or update the fusionmodel by adjusting the weight factors to be proportional to the computedcorrelations.
 11. The system of claim 1, wherein the reference painquantification includes a pain perception curve, and wherein thecalibration circuit is configured to: generate psychometric curves usingmeasurements of a plurality of signal metrics of the sensed plurality ofphysiological or functional signals corresponding to the plurality ofpain scales; and establish or update the fusion model based on alignmentmetrics between the pain perception curve and the generated psychometriccurves.
 12. The system of claim 11, wherein: the pain analyzer circuitis configured to generate the pain score using a combination of theplurality of signal metrics weighted by the respective plurality ofweight factors; and the calibration circuit is configured to establishor update the fusion model by adjusting the weight factors to beproportional to the alignment metrics between the pain perception curveand the generated psychometric curves.
 13. A method for managing pain ofa patient using an implantable neuromodulator device (IND), the methodcomprising: sensing a plurality of physiological or functional signalscorresponding to multiple pain intensities from a patient using one ormore sensors; generating a reference pain quantification correspondingto the multiple pain intensities; establishing or updating a fusionmodel based on a correlation between (1) measurements from the pluralityof physiological or functional signals corresponding to the multiplepain intensities and (2) the reference pain quantification correspondingto the multiple pain intensities; generating a pain score using theestablished or updated fusion model that combines the sensed pluralityof the physiological or functional signals; and outputting the painscore to a user or a process.
 14. The method of claim 13, furthercomprising delivering a pain therapy via the IND, the pain therapyincluding electrostimulation energy determined according to the painscore.
 15. The method of claim 13, further comprising delivering aprogrammed electrostimulation to a target tissue to induce a painepisode with the multiple pain intensities, wherein the fusion model isestablished or updated based on the measurements from the plurality ofphysiological or functional signals during the induced pain episode andthe reference pain quantification during the induced pain episode. 16.The method of claim 13, further comprising executing a stress test toinduce a pain episode with the multiple pain intensities, wherein thefusion model is established or updated based on the measurements fromthe plurality of physiological or functional signals during the inducedpain episode and the reference pain quantification during the inducedpain episode.
 17. The method of claim 13, wherein the reference painquantification comprises a user input of perceived pain scalescorresponding to the multiple pain intensities.
 18. The method of claim13, wherein the reference pain quantification comprises quantifiedfunctional scores corresponding to the multiple pain intensities. 19.The method of claim 13, wherein: the reference pain quantificationincludes a plurality of pain scales; and the fusion model includes acombination of a plurality of signal metrics weighted by a respectiveplurality of weight factors; and the establishing or updating the fusionmodel further includes establishing or updating the weight factors to beproportional to correlations between the plurality of pain scales andmeasurements from the plurality of physiological or functional signalscorresponding to the plurality of pain scales.
 20. The method of claim13, wherein: the reference pain quantification includes a painperception curve; and the fusion model includes a combination of aplurality of signal metrics weighted by a respective plurality of weightfactors; and the establishing or updating the fusion model furtherincludes: generating psychometric curves using measurements of aplurality of signal metrics of the sensed plurality of physiological orfunctional signals corresponding to the plurality of pain scales; andestablishing or updating the weight factors to be proportional toalignment metrics between the pain perception curve and the generatedpsychometric curves.